predict.comprisk {timereg} | R Documentation |
Make predictions based on the survival models (Aalen and Cox-Aalen) and the competing risks models for the cumulative incidence function (comp.risk). Computes confidence intervals and confidence bands based on resampling.
## S3 method for class 'comprisk': predict(object,newdata=NULL,X=NULL,Z=NULL, n.sim=500,uniform=TRUE,se=TRUE,alpha=0.05,...)
object |
an object belonging to one of the following classes: comprisk, aalen or cox.aalen |
newdata |
specifies the data at which the predictions are wanted. |
X |
alternative to newdata, specifies the nonparametric components for predictions. |
Z |
alternative to newdata, specifies the parametric components of the model for predictions. |
n.sim |
number of simulations in resampling. |
uniform |
computes resampling based uniform confidence bands. |
se |
computes pointwise standard errors |
alpha |
specificies the significance levelwhich cause we consider. |
... |
unused arguments - for S3 compatability |
Thomas Scheike, Jeremy Silver
Scheike, Zhang and Gerds (2007), Predicting cumulative incidence probability by direct binomial regression, Biometrika, to appear.
Scheike and Zhang (2007), Flexible competing risks regression modelling and goodness of fit, work in progress.
Martinussen and Scheike (2006), Dynamic regression models for survival data, Springer.
data(bmt); times<-bmt$time[bmt$cause==1]; add<-comp.risk(Surv(time,cause>0)~platelet+age+tcell,bmt, bmt$cause,times[-1],causeS=1,resample.iid=1) summary(add) par(mfrow=c(2,4)) plot(add); plot(add,score=1) ndata<-data.frame(platelet=c(1,0,0),age=c(0,1,0),tcell=c(0,0,1)) par(mfrow=c(2,3)) out<-predict(add,ndata,uniform=1,n.sim=1000) par(mfrow=c(2,2)) plot(out,multiple=0,uniform=1,col=1:3,lty=1,se=1) # see comp.risk for further examples. ## SURVIVAL predictions aalen function data(sTRACE) out<-aalen(Surv(time,status==9)~const(age)+const(sex)+ const(diabetes)+chf+vf, sTRACE,max.time=7,n.sim=0,resample.iid=1) out<-predict(out,X=rbind(c(1,0,0),c(1,1,0)), Z=rbind(c(55,0,1),c(60,1,1))) par(mfrow=c(2,2)) plot(out,multiple=1,se=0,uniform=0,col=1:2,lty=1:2) plot(out,multiple=0,se=1,uniform=1,col=1:2) data(sTRACE) out<-cox.aalen(Surv(time,status==9)~prop(age)+prop(sex)+ prop(diabetes)+chf+vf, sTRACE,max.time=7,n.sim=0,resample.iid=1) out<-predict(out,X=rbind(c(1,0,0),c(1,1,0)), Z=rbind(c(55,0,1),c(60,1,1))) par(mfrow=c(2,2)) plot(out,multiple=1,se=0,uniform=0,col=1:2,lty=1:2) plot(out,multiple=0,se=1,uniform=1,col=1:2)